Abstract
Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 x 10(9) cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 x 10(9) cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer.

Abstract
Listeria monocytogenes has been referred to as a concern microorganism in cheese making due to its ability to survive and grow in a wide range of environmental conditions, such as refrigeration temperatures, low pH and high salt concentration at the end of the production process. Since cheese may be a potential hazard for consumers, especially high-risk consumers (e.g., pregnant, young children, the elderly, people with medical conditions), efforts of the dairy industry have been aimed at investigating new conservation techniques based on natural additives to meet consumers' demands on less processed foods without compromising the food safety. Thus, the aim of this study was to evaluate the efficacy of Myrtus communis L. (myrtle) and Rosmarinus officinalis L. (rosemary) essential oils (EO) against Listeria monocytogenes ATCC 679 spiked in sheep cheese before ripening. After the cheesemaking process, the samples were stored at 8 degrees C for 2 h, 1 d, 3 d, 14 d and 28 d. The composition of EO was identified by gas chromatography-mass spectrometry (GC-MS) analysis. Constituents such as 1,8-cineole, limonene, methyl-eugenol, alpha-pinene, alpha-terpineol, alpha-terpinolene and beta-pinene were present in both EO, accounting for 44.61% and 39.76% from the total of chemical compounds identified for myrtle and rosemary EO, respectively. According to the chemical classification, both EO were mainly composed of monoterpenes. Minimum inhibitory concentration (MIC) against L. monocytogenes was obtained at 31.25 mu L/mL to myrtle EO and at 0.40 mu L/mL to rosemary EO. Then, cheeses were inoculated with L. monocytogenes (Ca. 6 log CFU/mL) and EO was added at MIC value. The addition of rosemary and myrtle EO displayed lower counts of L. monocytogenes (p < 0.01) (about 1-2 log CFU/g) during the ripening period compared to control samples. Ripening only influences (p < 0.001) the growth of L. monocytogenes in control samples. Since rosemary and myrtle EO do not exert any negative impact on the growth of native microflora (p > 0.05), their use as natural antimicrobial additives in cheese demonstrated a potential for dairy processors to assure safety against L. monocytogenes.

Abstract
Functional organic dyes play a key role in many fields, namely in biotechnology and medical diagnosis. Herein, we report two novel 2,3- and 3,4-dihydroxyphenyl substituted rosamines (3 and 4, respectively) that were successfully synthesized through a microwave-assisted protocol. The best reaction yields were obtained for rosamine 4, which also showed the most interesting photophysical properties, specially toward biogenic amines (BAs). Several amines including n- and t-butylamine, cadaverine, and putrescine cause spectral changes of 4, in UV-Vis and fluorescence spectra, which are indicative of their potential application as an effective tool to detect amines in acetonitrile solutions. In the gas phase, the probe response is more expressive for spermine and putrescine. Additionally, we found that methanolic solutions of rosamine 4 and n-butylamine undergo a pink to yellow color change over time, which has been attributed to the formation of a new compound. The latter was isolated and identified as 5 (9-aminopyronin), whose solutions exhibit a remarkable increase in fluorescence intensity together with a shift toward more energetic wavelengths. Other 9-aminopyronins 6a, 6b, 7a, and 7b were obtained from methanolic solutions of 4 with putrescine and cadaverine, demonstrating the potential of this new xanthene entity to react with primary amines.

Abstract
This study analyzed the potential of proximal optical sensing as an effective approach for early disease detection. A compact, modular sensing system, combining direct UV–Vis spectroscopy with optical fibers, supported by a principal component analysis (PCA), was applied to evaluate the modifications promoted by the bacteria Xanthomonas euvesicatoria in tomato leaves (cv. cherry). Plant infection was achieved by spraying a bacterial suspension (108 CFU mL-1) until run-off occurred, and a similar approach was followed for the control group, where only water was applied. A total of 270 spectral measurements were performed on leaves, on five different time instances, including pre- and post-inoculation measurements. PCA was then applied to the acquired data from both healthy and inoculated leaves, which allowed their distinction and differentiation, three days after inoculation, when unhealthy plants were still asymptomatic.

Abstract
In hydroponic cultivation, monitoring and quantification of nutrients is of paramount importance. Precision agriculture has an urgent need for measuring fertilization and plant nutrient uptake. Reliable, robust and accurate sensors for measuring nitrogen (N), phosphorus (P) and potassium (K) are regarded as critical in this process. It is vital to understand nutrients’ interference; thusly, a Hoagland fertilizer solution-based orthogonal experimental design was deployed. Concentration ranges were varied in a target analyte-independent style, as follows: [N] = [103.17–554.85] ppm; [P] = [15.06–515.35] ppm; [K] = [113.78–516.45] ppm, by dilution from individual stock solutions. Quantitative results for N and K, and qualitative results for P were obtained.